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India International Conference on Power Electronics (IICPE)

作者： Agrima Chandra Prema Gaur Anuradha Tomar Department of Electrical Engineering Netaji Subhas University of Technology New Delhi India Department of Instrumentation and Control Engineering Netaji Subhas University of Technology New Delhi India

The designing and modeling of renewable based Electric Vehicle Charging Station (EVCS), using Solar Photovoltaics (SPV), SEPIC converter along with Maximum Power Point Tracking (MPPT) control, a BUCK converter as Voltage Regulation (VR) mechanism and Constant Current (CC) and Constant Voltage (CV) battery control for Li-ion battery charging which here is assumed as EV battery is proposed. A detailed modeling of the system is done in MATLAB/SIMULINK. The increase in Electric Vehicle (EV) has led to increasing demand of EVCS. The design proposed here is for off grid standalone charging station with renewable source integration enhancing green and clean energy source for sustainable transportation. Standalone systems are required to power the charging stations where grid feasibility is not available like in rural areas. SEPIC along with MPPT algorithm is used to dynamically optimize operating points of SPV to achieve maximum energy under different environmental conditions. In this paper a sustainable power generation system for Electric Vehicle Charging Station (EVCS) is proposed. The adoption of FLC for CC-CV control is advantageous over PID control in dealing with nonlinearities and adapting real time conditions parameters in changing environment. The performance and feasibility of system is demonstrated via results of simulations in SIMULINK. The dynamic responses of FLC based CC-CV charging system and overall system is observed.

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An Investigation on Internet of Things: Smart Grid Technology Framework's

An Investigation on Internet of Things: Smart Grid Technolog...

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International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)

作者： Prabhat Srivastava Alok Agrawal Prerna Gaur Department of Electrical Engineering Netaji Subhas University of Technology New Delhi India Department of Instrumentation and Control Engineering Netaji Subhas University of Technology New Delhi India

The traditional grid networks could be upgraded to smart grids via Internet of Things (IoT) technology. This paper discusses the various grid system architecture's, technologies, and latest communication protocols for the IoT enabled smart grids. Literature survey shows that the technological advancements in power systems are making the grids better in terms of stability and sustainability; but one of the major issue related to such advancements, especially in the IoT enabled smart grids is related to data theft. This paper summarizes the IoT enabled mitigation techniques for such smart grid system vulnerabilities. Use of technologies such as bigdata, machine learning, augmented reality and blockchain, can give support to the IoT smart energy systems so that it become more flexible, safe, and secure. This paper summarizes the knowledge of such advanced technologies which are indeed helpful to make the IoT enabled smart grid system more effective technically, economically, and socially.

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Detailed Performance Analysis of SPV Array Configurations under Partial Shading Conditions 1

Detailed Performance Analysis of SPV Array Configurations un...

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1st International Conference on Industrial Electronics Research and Applications, ICIERA 2021

作者： Shah Beg, Mirza Tabish Rana, K.P.S. Kumar, Vineet Netaji Subhas University of Technology Department of Instrumentation and Control Engineering Dwarka New Delhi India

ISBN: (纸本)9781665435420

The primary objective of this work is to present a detailed performance analysis of various 2×2 solar photovoltaic (SPV) array configurations, i.e., series (S), parallel (P), series-parallel (SP), and total-cross-Tied (TCT) under different shading conditions. It may be noted that a good amount of literature already exists on this problem. Various performance parameters such as peak power output, the voltage at maximum power point (MPP), current at MPP, mismatch losses, relative power losses, and the fill factor have been reported. But, a quick survey reveals that all these parameters, though very important, have not been investigated together, leading to a complete performance investigation. This very fact has been the primary motivation to take up this work. In addition, a systematic procedure is developed for designing and developing various interconnection schemes to thoroughly analyze most of the performance parameters for different shading conditions. This work is expected to be very useful for beginners, particularly those who want further research in this area. The presented investigation revealed the following. The series configuration offers lower current and power outputs under partial shading conditions (PSC) while having multiple peak power-voltage (P-V) curves leading to maximum power point tracking (MPPT) problem. Further, the parallel configuration has a single peak in the P-V curve leading to better performance than other configurations under PSC. However, it also suffers from lower voltage output. The SP configuration offered better performance than the above two schemes under PSC. The best configuration has been the TCT yielding moderate current and voltage outputs at MPP, higher power output at MPP, lower relative power losses, lower mismatch losses, and improved fill factor. Therefore, the TCT configuration has better performance than other interconnection schemes under PSC. © 2021 IEEE.

关键词： Maximum power point trackers

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A pseudo-labelled ensemble inference for fast and efficient real field photovoltaic system's power prediction

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Optik 2023年第1期287卷

作者： Dhingra, Bhavya Tyagi, Shivam Verma, Aryan Tomar, Anuradha Rani, Poonam Department of Electrical Engineering Netaji Subhas University of Technology Dwarka Delhi110078 India Department of Computer Science Netaji Subhas University of Technology Dwarka Delhi110078 India Department of Instrumentation and Control Engineering Netaji Subhas University of TechnologyDwarka Delhi110078 India

Clean energy solutions like solar power plants have been at the forefront of the collaborative global efforts of the world towards the sustainable development of society. This research paper explores the use of machine learning in predicting photovoltaic (PV) power generation. An accurate and reliable prediction of solar power generation is crucial for the efficient integration of clean energy solutions into the existing power grid. The most common approach for PV forecasting is the statistical approach, which captures the local weather, seasonal, and geographical effects on the PV system but has limited accuracy due to limited data. The physical approach uses complex physical models to simulate solar radiation and predict energy output, but it is time-consuming and unsuitable for short-term forecasting. This study proposed a 3-stage machine learning architecture that uses pseudo labelling to increase accuracy and efficiency in predicting power generated by PV modules. The architecture uses a light gradient boosting machine (LGBM) and Cuda random forest (cuRF) together, which improved the model performance and achieved a high accuracy score of 99.98% R2. This study addresses missing data points and improves accuracy using imputation and weighted ensemble techniques with a year's worth of institutional data. The proposed model outperforms other models tested in this study. © 2023

关键词： Machine learning

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A Comparative Analysis of Different Batteries Technology on Off-Grid Hybrid System

A Comparative Analysis of Different Batteries Technology on ...

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Measurement, instrumentation, control and Automation (ICMICA), 2020 First IEEE International Conference on

作者： Avinash Gaurav Arjun Tyagi Bhavnesh Kumar Shyama Kant Jha Department of Electrical Engineering Netaji Subhash University of Technology New Delhi India Department of Electrical Engineering Faculty of Technology University of Delhi Delhi India Department of Instrumentation & Control Engineering Netaji Subhas University of Technology New Delhi India

ISBN: (数字)9798350355048

ISBN: (纸本)9798350355055

Several rural communities are still living with disrupted power supplies. Consequently, diesel generators are used for getting regular power supply to provide effective healthcare services. Usage of diesel generator not only have deteriorating impact on the environment, but also is an expensive alternative to grid supply. Usage of renewable energy systems with battery are becoming popular solution for feeding such kind of loads in the remote areas. In this work, an off-grid hybrid electrification system (HES) is designed which consists of Solar PV-Diesel Generator-Batteries for the regular supply of electricity to the available load of PHC, located in a remote area of Kaimur district in Bihar, India. Investigation is carried out for five different technologies of batteries Lead-acid (LA), Lithium-ion (LI), Vanadium flow (VF), Zinc bromide (ZB) and Nickel-iron (NI) which are incorporated with HES. An optimum configuration for a PHC has been designed taking all major techno-economic factors and renewable penetration into consideration. Investigation reveals that a vanadium flow-based hybrid electrification system is the most feasible solution with respect to the techno-economic perspective among all considered configurations.

关键词： Renewable energy sources Costs Power supplies Electricity Vanadium Computer architecture Electrification Generators Batteries Zinc

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Multi-model approach applied to meteorological data for solar radiation forecasting using data-driven approaches

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Optik 2023年第1期286卷

作者： Neeraj Gupta, Pankaj Tomar, Anuradha Department of Electronics and Communication Engineering Indira Gandhi Delhi Technical University for Women India Department of Instrumentation and Control Engineering Netaji Subhas University of Technology India

The accuracy of the solar forecasting model depends upon various factors such as attributes of the data, time horizon, time interval, length of the data, etc. This means attributes of data selection are an important feature of any solar forecasting model. A lot of research has been done on solar forecasting using deep learning and machine learning approaches, providing better accuracy for a particular type of model and data. However, how to accurately apply the multi-model techniques in the data-driven approaches of the solar radiation prediction model is still a challenging issue due to the complexity of the temporal resolution, length, and attribute selection of the data. This paper proposes a multi-model technique using data-driven approaches to get an improvised solar forecasting model with respect to the attributes of data. In this research, a multi-model method based on data-driven approaches is developed for short-term solar radiation forecasting. Three years of weather data with different temporal resolutions taken from reliable online sources were used for training and testing in this work. The weather data considered various input parameters such as temperature, humidity, pressure, wind speed, and wind direction;solar radiation data is considered the model's target variable. This work is a two-stage process. In the first stage, multi-model methods are analyzed based on solar forecasts at different temporal resolutions, and best-performing models are selected. The optimization technique is applied to the best model in the second stage to obtain an improvised solar forecasting model. It is observed that the multi-model forecasts provide better accuracy in terms of R2, nRMSE, and MAE than the conventional forecasting model. In this work, the performance parameters of nRMSE = 0.34, MAE = 4.71, and R2 = 0.93 are obtained before optimization, and nRMSE = 0.25, MAE = 1.39 and R2 = 0.96 are obtained after optimization in the testing stage for an hourly dataset. Fo

关键词： Solar energy

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A Comparison of Hourly Solar Energy Generation Forecasting Using RNN and LSTM Network 1

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3rd International Conference on Machine Learning, Advances in Computing, Renewable Energy and Communication, MARC 2021

作者： Neeraj Gupta, Pankaj Tomar, Anuradha Department of Electronics and Communication Engineering Indira Gandhi Delhi Technical University for Women Delhi New Delhi India Department of Instrumentation and Control Engineering Netaji Subhas University of Technology Delhi New Delhi India

ISBN: (数字)9789811928284

ISBN: (纸本)9789811928277

Nowadays, various advanced techniques, like big data and deep learning, have been used in energy management systems for improving energy efficiency. Various research has been done on solar power generation prediction using multiple machine learning approaches. However, it is not easy to make exact predictions due to the alternative nature of solar energy. Therefore, this work aims to accurately implement and compare the neural network models to predict solar power generation. The models in this study are implemented using Python, TensorFlow, and Keras. The performance of the models is evaluated using 500 KWp grid-connected plant data. This work forecasted a solar power generation for 72 h using a time series dataset. Approximately, three years of the dataset are collected from January 2019 to October 2021 with hourly resolution from Airport Depot, Delhi, India. This paper compared the performance of the recurrent neural network (RNN) and long short-term memory (LSTM) in the form of accuracy. The three-year energy generation dataset is split into train and test data. The train data uses three years of energy generation data from January 2019 to September 2021, while the test data are used for October 2021. As a result, the determinant of coefficient (R 2 ) is a statistical error matrix representing the predicted values’ accuracy. The results show that the LSTM model predicts solar power generation with high accuracy (R 2 = 0.92). © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Solar energy

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On comparing the performances of MLP and RBFN on sales forecasting problem

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International Journal of Information Technology (Singapore) 2022年第1期14卷 301-309页

作者： Tiwari, Raman Kumar, Rajesh Gera, Rajat Srivastava, Smriti Faculty of Management Studies Manav Rachna University Faridabad India Department of Electrical and Instrumentation Engineering Thapar Institute of Engineering and Technology Patiala 147004 India Faculty of Management Studies Manav Rachna International Institute of research and studies Faridabad India Division of Instrumentation and Control Netaji Subhas University of Technology (formerly Netaji Subhas Institute of Technology) New Delhi 110078 India

This paper addresses the problem of predicting the sales by developing two sales forecasting models based on multi-layered perceptron (MLP) and radial basis function network (RBFN). The performance of both these models have been thoroughly compared in predicting the sales. To update the parameters in these models we have used dynamic back-propagation (DBP) method. Simulation results obtained shows that MLP has performed better than the RBFN. © 2019, Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Dynamic back-propagation algorithm MLP prediction RBFN Sales forecasting

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Metaheuristic Algorithm Implementation for PV Array Reconfiguration under Realistic Moving Cloud Condition

Metaheuristic Algorithm Implementation for PV Array Reconfig...

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Device Intelligence, Computing and Communication Technologies, (DICCT), International Conference on

作者： Mridul Kapur Agamdeep Singh Mahal Priyanshu Kumar Diwaker Pathak Manisha Prerna Gaur Instrumentation and Control Engineering Department Netaji Subhas University of Technology (Formerly NSIT University of Delhi) New Delhi India Department of Electrical Engineering Faculty of Engineering Teerthanker Mahaveer University Moradabad UP India Instrumentation and Control Engineering Department Netaji Subhas University of Technology New Delhi India

Partial shading imposes a major issue while generating power using solar photovoltaics (SPV) and consequences in the formation of hot spots and manifold peaks in the power-voltage (P-V) curve of the SPV arrays. As a result, a main deforming concern arises i.e., The power loss due to mismatch in the SPV system is dependent on and directly caused by the shading pattern. Mitigation of this MPL can be assessed by selecting a proper configuration of the SPV array and optimal shading dispersion over the complete SPV array. This paper focuses on the implementation of renowned metaheuristic algorithms (MAs) to optimally disperse the shading effect for a partially shaded total-cross-tied (TCT) SPV array configuration. A partial shading effect is generated using a realistic moving cloud model by incorporating various affecting environmental parameters. Furthermore, MAs are tested for the designed moving cloud model and a comprehensive analysis is presented among the various SPV array configurations.

关键词： Performance evaluation Multiprotocol label switching Manifolds Analytical models Uncertainty Fill factor (solar cell) Metaheuristics

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Efficient Patient Independent Seizure Detection System using WAF based Hybrid Feature Extraction Method and XGBoost classifier

Efficient Patient Independent Seizure Detection System using...

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2022 IEEE Delhi Section Conference, DELCON 2022

作者： Nandini, Durgesh Yadav, Jyoti Rani, Asha Singh, Vijander Kravchenko, Oleg V. Netaji Subhas University of Technology Department Instrumentation and Control Engineering New Delhi India Bauman Moscow State Technical University Frc Csc Ras Higher Mathematics Department Moscow Russia

ISBN: (纸本)9781665458832

This work presents a segment-based patient-independent (PI) seizure detection system using hybrid feature extraction method. The segment-based approach offers increased applicability across various seizure patients compared to the patient-dependent (PD) approach. The proposed work undertakes the ictal and inter-ictal seizure stages. The designed system is optimized for EEG channels using the Principal Component Analysis (PCA) technique as it will reduce the computation cost and the complexities of the classification algorithm. In addition to this, the trends in the non-stationary EEG signal are analysed using the Singular Spectrum Analysis (SSA) technique. Further novel application of Wavelet-based on atomic function (WAF) is presented for feature extraction. WAF is an established technique in literature, used in many engineering applications. Hence, the proposed work employs WAF-based hybrid feature extraction methods used in combination with SSA and time-domain feature extraction techniques to retrieve the features from the EEG signals. Further various Machine Learning (ML) techniques have been used for classification. The results reveal the highest classification accuracy of 94.46% using the scalable tree boosting algorithm. The reported results are 19.86% and 7.76% higher compared to state-of-the-art methods present in the literature. © 2022 IEEE.

关键词： Principal component analysis

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